Intelligent Path Planning for Robotic Systems: Modeling, Optimization and Real-Time Decision-Making

Dear Colleagues,

Optimization algorithms are pivotal in enabling robots to achieve autonomy in complex, dynamic environments. While traditional methods often fail to address scalability and real-time demands, emerging techniques—from metaheuristics (PSO, GA, ACO) to combinatorial optimization and reinforcement learning—are revolutionizing robotic path planning. However, their practical deployment requires tight integration with environmental perception, robust decision-making under uncertainty, and computationally efficient execution.

This Special Issue focuses on algorithmic innovation and system-level implementation for robotic path planning, emphasizing three interconnected pillars:

(1) Modeling and Optimization

• Advanced formulations for kinodynamic/dynamic constraints in continuous/discrete spaces;
• Multi-objective optimization (e.g., energy-time-risk tradeoffs) with interpretability guarantees;
• Hybrid architectures combining classical optimization with learning-based components.

(2) Real-Time Planning and Decision-Making

• Online replanning with provable latency bounds (e.g., anytime algorithms, model predictive control);
• Adaptive strategies for dynamic obstacles and uncertain environments (e.g., stochastic RL, meta-learning);
• Hardware–algorithm co-design (e.g., edge computing, FPGA acceleration).

(3) Environmental Intelligence for Planning

• Active perception–modeling–planning loops (e.g., uncertainty-aware mapping);
• Human-aware navigation (e.g., social cost maps, intent prediction);
• Physics-informed terrain interaction (e.g., deformable surfaces, fluid dynamics).

We seek contributions that

• Propose novel algorithms with theoretical rigor and practical validation (simulation + hardware);
• Address real-world challenges, such as sensor noise, computation/communication bottlenecks, safety-critical constraints;
• Demonstrate applications in autonomous vehicles, agile robots, search-and-rescue, or other latency-sensitive domains.

Dr. Jingrui Zhang
Dr. Yu Xie
Dr. Po Li
Guest Editors

Manuscript Submission Information

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• kinodynamic planning
• anytime algorithms
• model predictive control
• chance-constrained optimization
• mul-ti-objective optimization
• subgoal replanning
• latency-aware scheduling
• uncertainty-aware mapping
• human–robot spatial cognition
• neuromorphic computing for planning
• physics-informed neural planners
• federated learn-ing for multi-robot systems
• bio-inspired navigation
• heuristic/meta-heuristic path planning